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Demonstration Summary Services provided to Boeing Challenge Problem Relationship to Project Evaluation Criteria  Our project is not focused on specific middleware services, rather the composition and synthesis technology. For both control strategies we’ve selected the set of appropriate mw services including group communication, discovery etc. We’ve created new services including publish/subscribe and neighborhood. We’ve used DISSECT to compose and synthesize these.  Our demo is a subset of the final CP. The actual technology and tools that we have developed are prototypes of the final system that will support all CPs Pattern-Oriented Composition and Synthesis of Middleware Services for NEST DISSECT-ing the Fairing Simulation Ledeczi Vanderbilt The middleware layer is modeled, composed and synthesized for two simulation setups using different control strategies  DISSECT : The tool supports modeling, composition and automatic synthesis of the middleware layer utilizing RT-CORBA. It is directly applicable to the Boeing fairing simulation platform.  New services :  NeighborhoodComm  Physical Location  Publish/Subscribe  Number of different target platforms supported  Number of different middleware services modeled, composed and synthesized  Application performance: time and frequency domain vibration attenuation  Middleware development time Boeing Simulation Engine DISSECT Geographic Ctrl Sim MWS Mode-Based Control Geographic Control MWS Mode-Based Ctrl Sim

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13 Comparison of Local and Distributed Control on the Boeing Fairing

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14 Objectives Compare the default Boeing simulator control approach to a distributed control approach Default control law applies dynamic compensation to local sensor signal only Distributed control law applies static compensation to many sensor signals

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15 Control based on Group Management Middleware Assuming that groups can be defined and managed –How well can specific modes be targeted? –What type of grouping yields the best performance? Each node receives sensor signals from all group members to produce local control signal Grouping based on structural modes or geographic neighbors

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20 Control Comparison Conclusion Comparison is not perfect –Unknown control effort comparison –Effects of network on distributed control are unknown (although previous simulations indicate that these effects are minimal) Performance of the two approaches are similar The two approaches challenge middleware in very different ways

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23 Demonstration Issues Low-level node to node communication with reach = 2 (i.e. ~12 neighbors for every node) overloaded Windows. Either <=25 nodes or reach = 1 or two machines had to be used. RT-CORBA imposes relatively large infrastructural overhead that may be prohibitive for most “classical” NEST platforms

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24 Demonstration Lessons Learned DISSECT language was developed before considering RT-CORBA as a target platform. Yet the modeling language is powerful enough to capture all necessary concepts for the new platform without modification Models are rich enough that would enable generation of custom marshalling code for severely resource constrained targets where the overhead of CORBA is prohibitive DISSECT provides nice support for componentization, complex components can be easily refactored into smaller units.